MMR-and-RMR-workflow-Clutch49
Elliott Schmidt
2024-08-16
Load libraries (packages)
System1 - Dell
Importing data from firesting for resting
preexperiment_date <- "30 June 2023 10 41AM/All"
postexperiment_date <- "30 June 2023 03 36PM/All"
##--- last fish run in trial ---##
experiment_date <- "30 June 2023 12 23PM/Oxygen"
experiment_date2 <- "30 June 2023 12 23PM/All"
firesting <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell7440/Experiment_",experiment_date,"data raw/Firesting.txt"),
delim = "\t", escape_double = FALSE,
col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"),
`Time (s)` = col_number(), Ch1...5 = col_number(),
Ch2...6 = col_number(), Ch3...7 = col_number(),
Ch4...8 = col_number()), trim_ws = TRUE,
skip = 19)
Cycle_1 <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell7440/Experiment_",experiment_date2,"slopes/Cycle_1.txt"),
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
`Seconds from start for linreg` = col_number(),
`ch1 po2` = col_number(), `ch2 po2` = col_number(),
`ch3 po2` = col_number(), `ch4 po2` = col_number(),
...8 = col_skip()), trim_ws = TRUE)
Cycle_last <-read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell7440/Experiment_",experiment_date2,"slopes/Cycle_21.txt"),
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
`Seconds from start for linreg` = col_number(),
`ch1 po2` = col_number(), `ch2 po2` = col_number(),
`ch3 po2` = col_number(), `ch4 po2` = col_number(),
...8 = col_skip()), trim_ws = TRUE) System2 - Asus
Importing data from firesting for resting
preexperiment_date_asus <- "30 June 2023 11 02AM/All"
postexperiment_date_asus <- "30 June 2023 04 26PM/All"
##--- last fish run in trial ---##
experiment_date_asus <- "30 June 2023 01 07PM/Oxygen"
experiment_date2_asus <- "30 June 2023 01 07PM/All"
firesting_asus <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_date_asus,"data raw/Firesting.txt"),
delim = "\t", escape_double = FALSE,
col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"),
`Time (s)` = col_number(), Ch1...5 = col_number(),
Ch2...6 = col_number(), Ch3...7 = col_number(),
Ch4...8 = col_number()), trim_ws = TRUE,
skip = 19)
Cycle_1_asus <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_date2_asus,"slopes/Cycle_1.txt"),
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
`Seconds from start for linreg` = col_number(),
`ch1 po2` = col_number(), `ch2 po2` = col_number(),
`ch3 po2` = col_number(), `ch4 po2` = col_number(),
...8 = col_skip()), trim_ws = TRUE)
Cycle_last_asus <-read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_date2_asus,"slopes/Cycle_21.txt"),
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
`Seconds from start for linreg` = col_number(),
`ch1 po2` = col_number(), `ch2 po2` = col_number(),
`ch3 po2` = col_number(), `ch4 po2` = col_number(),
...8 = col_skip()), trim_ws = TRUE) Chamber volumes
chamber1_dell = 0.04650#+0.00022
chamber2_dell = 0.04593#+0.00022
chamber3_dell = 0.04977#+0.00022
chamber4_dell = 0.04860#+0.00022
chamber1_asus = 0.04565
chamber2_asus = 0.04573#+0.00385
chamber3_asus = 0.04551#+0.00322
chamber4_asus = 0.04791#+0.00277
Date_tested="2023-06-30"
Clutch = "132"
Male = "CVLA468"
Female = "CVLA477"
Population = "Vlassof reef"
Tank =129
salinity =35
Date_analysed = Sys.Date() Replicates
2
Enter specimen data
Replicate = 2
mass = 0.0006440
chamber = "ch3"
Swim = "good/good"
chamber_vol = chamber3_dell
system1 = "Dell7440"
Notes=""
##--- time of trail ---##
experiment_mmr_date <- "30 June 2023 11 57AM/Oxygen"
experiment_mmr_date2 <- "30 June 2023 11 57AM/All"
firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell7440/Experiment_",experiment_mmr_date,"data raw/Firesting.txt"),
delim = "\t", escape_double = FALSE,
col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"),
`Time (s)` = col_number(), Ch1...5 = col_number(),
Ch2...6 = col_number(), Ch3...7 = col_number(),
Ch4...8 = col_number()), trim_ws = TRUE,
skip = 19) ## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell7440/Experiment_",experiment_mmr_date2,"slopes/Cycle_1.txt"),
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
`Seconds from start for linreg` = col_number(),
`ch1 po2` = col_number(), `ch2 po2` = col_number(),
`ch3 po2` = col_number(), `ch4 po2` = col_number(),
...8 = col_skip()), trim_ws = TRUE) ## New names:
## • `` -> `...8`Background rates
Pre-experiment
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell7440/Experiment_",preexperiment_date,"slopes"))
pre_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_pre1 <- pre_cycle1 %>% calc_rate.bg()##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------## [1] -0.001233486post-experiment
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell7440/Experiment_",postexperiment_date,"slopes"))
post_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_post1 <- post_cycle1 %>% calc_rate.bg() ##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------## [1] -0.001860248Resting metabolic rate
Data manipulation
Inspect file
## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch3
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 8 9 10 12 13 14 15 16 18 19 20 21 22 23
## Minimum and Maximum intervals in uneven Time data:
## [1] 0.94 1.49
## -----------------------------------------
#### subset data
Tstart.row=which(firesting2$TIME ==Cycle_1$Time[1], firesting$TIME)
Tstart.dTIME=as.numeric(firesting2[Tstart.row, "dTIME"])
Tend.row=which(firesting2$TIME ==tail(Cycle_last$Time, n=1), firesting$TIME)
Tend.dTIME=as.numeric(firesting2[Tend.row, "dTIME"])
apoly_insp <- firesting2 |>
subset_data(from=Tstart.dTIME,
to=Tend.dTIME,
by="time")
apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)##
## # print.inspect # -----------------------
## dTIME ch3
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 2 3 4 5 6 8 9 10 11 12 106 107 114 115 116 117 120 121 124
## [20] 125
## Minimum and Maximum intervals in uneven Time data:
## [1] 0.95 1.09
## -----------------------------------------
Extract rates
apoly_cr.int <- calc_rate.int(apoly_insp,
starts=(195+45+300),
wait=15,
measure=255,
by="time",
plot=TRUE) ##
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.
## -----------------------------------------##
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from selected replicates...
## To plot others modify 'pos' input.
## -----------------------------------------adjust rates for background
apoly_cr.int_adj <- adjust_rate(apoly_cr.int,
by = bg_pre,
by2 = bg_post,
time_by = Tstart.row,
time_by2 = Tend.row,
method = "linear")## adjust_rate: Rate adjustments applied using "linear" method.Converting units
apoly_cr.int_adj2 <- apoly_cr.int_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253) ## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.Plot curve
ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) +
geom_point() +
stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
theme_classic()
Rate filtering
apoly_rmr <- apoly_cr.int_adj2 |>
select_rate(method ="rsq", n=c(0.95,1)) |>
select_rate(method="lowest", n=6) |>
plot(type="full") |>
summary(export = TRUE)## select_rate: Selecting rates with rsq values between 0.95 and 1...## ----- Selection complete. 0 rate(s) removed, 21 rate(s) remaining -----## select_rate: Selecting lowest 6 *absolute* rate values...## ----- Selection complete. 15 rate(s) removed, 6 rate(s) remaining -----##
## # plot.convert_rate # -------------------## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: 7 1 330.0194 -0.02438732 0.996 NA 3256 3511 9495.07
## 2: 13 1 428.5024 -0.02590950 0.997 NA 6496 6751 12735.07
## 3: 14 1 426.9774 -0.02475026 0.997 NA 7036 7291 13275.07
## 4: 16 1 466.7455 -0.02565470 0.995 NA 8116 8371 14355.07
## 5: 17 1 487.2977 -0.02609823 0.996 NA 8656 8911 14895.07
## 6: 19 1 510.9361 -0.02583037 0.991 NA 9736 9991 15975.07
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 9750.07 98.290 92.079 -0.02438732 -0.001424748 -0.02296257 -0.02296257
## 2: 12990.07 98.486 91.681 -0.02590950 -0.001607678 -0.02430182 -0.02430182
## 3: 13530.07 98.224 91.858 -0.02475026 -0.001638167 -0.02311209 -0.02311209
## 4: 14610.07 98.251 91.661 -0.02565470 -0.001699144 -0.02395556 -0.02395556
## 5: 15150.07 98.355 91.978 -0.02609823 -0.001729632 -0.02436859 -0.02436859
## 6: 16230.07 98.370 91.730 -0.02583037 -0.001790609 -0.02403976 -0.02403976
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.04977 0.000644 NA 35 27 1.013253 -0.2685898
## 2: %Air sec 0.04977 0.000644 NA 35 27 1.013253 -0.2842547
## 3: %Air sec 0.04977 0.000644 NA 35 27 1.013253 -0.2703387
## 4: %Air sec 0.04977 0.000644 NA 35 27 1.013253 -0.2802046
## 5: %Air sec 0.04977 0.000644 NA 35 27 1.013253 -0.2850358
## 6: %Air sec 0.04977 0.000644 NA 35 27 1.013253 -0.2811895
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -417.0649 NA mgO2/hr/kg -417.0649
## 2: -441.3893 NA mgO2/hr/kg -441.3893
## 3: -419.7805 NA mgO2/hr/kg -419.7805
## 4: -435.1003 NA mgO2/hr/kg -435.1003
## 5: -442.6022 NA mgO2/hr/kg -442.6022
## 6: -436.6297 NA mgO2/hr/kg -436.6297
## -----------------------------------------Results
results <- data.frame(Clutch = Clutch,
Replicate =Replicate,
Male=Male,
Female=Female,
Population = Population,
Tank = Tank,
Mass = mass,
Chamber = chamber,
System = system1,
Volume = chamber_vol,
Date_tested = Date_tested,
Date_analysed =Date_analysed,
Swim = Swim,
Salinity = salinity,
Temperature = as.numeric(unique(firesting2$temperature)),
Resting_kg = mean(apoly_rmr$rate.output*-1),
Resting = mean(apoly_rmr$rate.output*-1)*mass,
rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 132 | 2 | CVLA468 | CVLA477 | Vlassof reef | 129 | 0.000644 | ch3 | Dell7440 | 0.04977 | 2023-06-30 | 2024-08-16 | good/good | 35 | 27 | 435.1004 | 0.2802047 | 0.9952 |
Maximum oxygen consumption
Data manipulation
Inspect file
## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch3
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 8 9 10 12 13 14 15 16 18 19 20 21 22 23
## Minimum and Maximum intervals in uneven Time data:
## [1] 0.94 1.49
## -----------------------------------------
Subset data
cycle1.start <- Cycle_1.mmr[1,1]
cycle1.end <- tail(Cycle_1.mmr, n=1)[1,1]
cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="cycle1_data <- firesting2_mmr |>
subset_data(from = cycle1.start.row,
to = cycle1.end.row,
by = "row") ## subset_data: Multi-column dataset detected in input!
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively.
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.## inspect: Applying column default of 'time = 1'## inspect: Applying column default of 'oxygen = 2'## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch3
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 26 27 31 32 36 37 38 42 43 44 45 46 47 48 49 50 51 52 53 54
## Minimum and Maximum intervals in uneven Time data:
## [1] 0.94 1.14
## -----------------------------------------
Calculating MMR
## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
## If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()
##
## # summary.auto_rate # -------------------
##
## === Summary of Results by Highest Rate ===
## rep rank intercept_b0 slope_b1 rsq density row endrow
## <num> <int> <num> <num> <num> <lgcl> <int> <int>
## 1: NA 1 357.5627 -0.05485041 0.9969911 NA 166 226
## 2: NA 2 357.3073 -0.05479792 0.9969368 NA 165 225
## 3: NA 3 356.9109 -0.05471538 0.9967988 NA 167 227
## 4: NA 4 356.7518 -0.05468328 0.9968751 NA 164 224
## 5: NA 5 356.4373 -0.05461711 0.9966395 NA 168 228
## ---
## 237: NA 237 180.5784 -0.01753167 0.9033745 NA 5 65
## 238: NA 238 178.6495 -0.01711933 0.9007586 NA 4 64
## 239: NA 239 176.3267 -0.01662277 0.8965072 NA 3 63
## 240: NA 240 174.2393 -0.01617623 0.8950409 NA 2 62
## 241: NA 241 172.2537 -0.01575148 0.8920472 NA 1 61
## time endtime oxy endoxy rate
## <num> <num> <num> <num> <num>
## 1: 4811.07 4871.07 93.654 90.409 -0.05485041
## 2: 4810.07 4870.07 93.668 90.423 -0.05479792
## 3: 4812.07 4872.07 93.578 90.433 -0.05471538
## 4: 4809.07 4869.07 93.723 90.454 -0.05468328
## 5: 4813.07 4873.07 93.467 90.381 -0.05461711
## ---
## 237: 4650.07 4710.07 98.943 97.860 -0.01753167
## 238: 4649.07 4709.07 98.960 97.875 -0.01711933
## 239: 4648.07 4708.07 98.928 97.893 -0.01662277
## 240: 4647.07 4707.07 98.976 97.948 -0.01617623
## 241: 4646.07 4706.07 98.969 97.966 -0.01575148
##
## Regressions : 241 | Results : 241 | Method : highest | Roll width : 60 | Roll type : time
## -----------------------------------------Adjusting
## adjust_rate: Rate adjustments applied using "mean" method.##
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
##
## Adjustment was applied using the 'mean' method.
##
## Rank 1 of 241 adjusted rate(s):
## Rate : -0.05485041
## Adjustment : -0.001233486
## Adjusted Rate : -0.05361693
##
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------Converting units
mmr_adj2 <- mmr_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253)## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.selecting rates
mmr_final <- mmr_adj2 |>
select_rate(method = "rsq", n=c(0.93,1)) |>
select_rate(method = "highest", n=1) |>
plot(type="full") |>
summary(export=TRUE)## select_rate: Selecting rates with rsq values between 0.93 and 1...## ----- Selection complete. 12 rate(s) removed, 229 rate(s) remaining -----## select_rate: Selecting highest 1 *absolute* rate values...## ----- Selection complete. 228 rate(s) removed, 1 rate(s) remaining -----##
## # plot.convert_rate # -------------------## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: NA 1 357.5627 -0.05485041 0.9969911 NA 166 226 4811.07
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 4871.07 93.654 90.409 -0.05485041 -0.001233486 -0.05361693 -0.05361693
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.04977 0.000644 NA 35 27 1.013253 -0.6271492
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -973.8341 NA mgO2/hr/kg -973.8341
## -----------------------------------------Results
results <- results |>
mutate(Max_kg = mmr_final$rate.output*-1,
Max = (mmr_final$rate.output*-1)*mass,
rsqmax =mmr_final$rsq,
AAS_kg = Max_kg - Resting_kg,
AAS = Max - Resting,
Notes=Notes,
True_resting="")
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest | Max_kg | Max | rsqmax | AAS_kg | AAS | Notes | True_resting |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 132 | 2 | CVLA468 | CVLA477 | Vlassof reef | 129 | 0.000644 | ch3 | Dell7440 | 0.04977 | 2023-06-30 | 2024-08-16 | good/good | 35 | 27 | 435.1004 | 0.2802047 | 0.9952 | 973.8341 | 0.6271492 | 0.9969911 | 538.7337 | 0.3469445 |
Exporting data
## Rows: 362 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.3
Enter specimen data
Replicate = 3
mass = 0.0009007
chamber = "ch2"
Swim = "good/good"
chamber_vol = chamber2_dell
system1 = "Dell7440"
Notes=""
##--- time of trail ---##
experiment_mmr_date <- "30 June 2023 12 23PM/Oxygen"
experiment_mmr_date2 <- "30 June 2023 12 23PM/All"
firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell7440/Experiment_",experiment_mmr_date,"data raw/Firesting.txt"),
delim = "\t", escape_double = FALSE,
col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"),
`Time (s)` = col_number(), Ch1...5 = col_number(),
Ch2...6 = col_number(), Ch3...7 = col_number(),
Ch4...8 = col_number()), trim_ws = TRUE,
skip = 19) ## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell7440/Experiment_",experiment_mmr_date2,"slopes/Cycle_1.txt"),
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
`Seconds from start for linreg` = col_number(),
`ch1 po2` = col_number(), `ch2 po2` = col_number(),
`ch3 po2` = col_number(), `ch4 po2` = col_number(),
...8 = col_skip()), trim_ws = TRUE) ## New names:
## • `` -> `...8`Background rates
Pre-experiment
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell7440/Experiment_",preexperiment_date,"slopes"))
pre_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_pre1 <- pre_cycle1 %>% calc_rate.bg()##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------## [1] -0.00278663post-experiment
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell7440/Experiment_",postexperiment_date,"slopes"))
post_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_post1 <- post_cycle1 %>% calc_rate.bg() ##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------## [1] -0.00189272Resting metabolic rate
Data manipulation
Inspect file
## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch2
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 8 9 10 12 13 14 15 16 18 19 20 21 22 23
## Minimum and Maximum intervals in uneven Time data:
## [1] 0.94 1.49
## -----------------------------------------
#### subset data
Tstart.row=which(firesting2$TIME ==Cycle_1$Time[1], firesting$TIME)
Tstart.dTIME=as.numeric(firesting2[Tstart.row, "dTIME"])
Tend.row=which(firesting2$TIME ==tail(Cycle_last$Time, n=1), firesting$TIME)
Tend.dTIME=as.numeric(firesting2[Tend.row, "dTIME"])
apoly_insp <- firesting2 |>
subset_data(from=Tstart.dTIME,
to=Tend.dTIME,
by="time")
apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)##
## # print.inspect # -----------------------
## dTIME ch2
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 2 3 4 5 6 8 9 10 11 12 106 107 114 115 116 117 120 121 124
## [20] 125
## Minimum and Maximum intervals in uneven Time data:
## [1] 0.95 1.09
## -----------------------------------------
Extract rates
apoly_cr.int <- calc_rate.int(apoly_insp,
starts=(195+45+300),
wait=45,
measure=255,
by="time",
plot=TRUE) ##
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.
## -----------------------------------------##
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from selected replicates...
## To plot others modify 'pos' input.
## -----------------------------------------adjust rates for background
apoly_cr.int_adj <- adjust_rate(apoly_cr.int,
by = bg_pre,
by2 = bg_post,
time_by = Tstart.row,
time_by2 = Tend.row,
method = "linear")## adjust_rate: Rate adjustments applied using "linear" method.Converting units
apoly_cr.int_adj2 <- apoly_cr.int_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253) ## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.Plot curve
ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) +
geom_point() +
stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
theme_classic()
Rate filtering
apoly_rmr <- apoly_cr.int_adj2 |>
select_rate(method ="rsq", n=c(0.95,1)) |>
select_rate(method="lowest", n=6) |>
plot(type="full") |>
summary(export = TRUE)## select_rate: Selecting rates with rsq values between 0.95 and 1...## ----- Selection complete. 0 rate(s) removed, 21 rate(s) remaining -----## select_rate: Selecting lowest 6 *absolute* rate values...## ----- Selection complete. 15 rate(s) removed, 6 rate(s) remaining -----##
## # plot.convert_rate # -------------------## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: 1 1 470.8955 -0.05963909 0.996 NA 46 301 6285.07
## 2: 2 1 518.4767 -0.06177390 0.998 NA 586 841 6825.07
## 3: 3 1 560.8004 -0.06305476 0.998 NA 1126 1381 7365.07
## 4: 4 1 659.2238 -0.07117622 0.998 NA 1666 1921 7905.07
## 5: 7 1 819.9499 -0.07609108 1.000 NA 3286 3541 9525.07
## 6: 21 1 1382.9425 -0.07542282 0.998 NA 10846 11101 17085.07
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 6540.07 95.945 80.323 -0.05963909 -0.002772331 -0.05686676 -0.05686676
## 2: 7080.07 96.322 80.781 -0.06177390 -0.002728847 -0.05904505 -0.05904505
## 3: 7620.07 96.032 80.130 -0.06305476 -0.002685363 -0.06036940 -0.06036940
## 4: 8160.07 95.701 78.270 -0.07117622 -0.002641880 -0.06853434 -0.06853434
## 5: 9780.07 94.918 75.549 -0.07609108 -0.002511429 -0.07357965 -0.07357965
## 6: 17340.07 94.449 75.300 -0.07542282 -0.001902659 -0.07352016 -0.07352016
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.04593 0.0009007 NA 35 27 1.013253 -0.6138414
## 2: %Air sec 0.04593 0.0009007 NA 35 27 1.013253 -0.6373547
## 3: %Air sec 0.04593 0.0009007 NA 35 27 1.013253 -0.6516503
## 4: %Air sec 0.04593 0.0009007 NA 35 27 1.013253 -0.7397858
## 5: %Air sec 0.04593 0.0009007 NA 35 27 1.013253 -0.7942468
## 6: %Air sec 0.04593 0.0009007 NA 35 27 1.013253 -0.7936046
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -681.5160 NA mgO2/hr/kg -681.5160
## 2: -707.6216 NA mgO2/hr/kg -707.6216
## 3: -723.4932 NA mgO2/hr/kg -723.4932
## 4: -821.3454 NA mgO2/hr/kg -821.3454
## 5: -881.8106 NA mgO2/hr/kg -881.8106
## 6: -881.0976 NA mgO2/hr/kg -881.0976
## -----------------------------------------Results
results <- data.frame(Clutch = Clutch,
Replicate =Replicate,
Male=Male,
Female=Female,
Population = Population,
Tank = Tank,
Mass = mass,
Chamber = chamber,
System = system1,
Volume = chamber_vol,
Date_tested = Date_tested,
Date_analysed =Date_analysed,
Swim = Swim,
Salinity = salinity,
Temperature = as.numeric(unique(firesting2$temperature)),
Resting_kg = mean(apoly_rmr$rate.output*-1),
Resting = mean(apoly_rmr$rate.output*-1)*mass,
rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 132 | 3 | CVLA468 | CVLA477 | Vlassof reef | 129 | 0.0009007 | ch2 | Dell7440 | 0.04593 | 2023-06-30 | 2024-08-16 | good/good | 35 | 27 | 803.0737 | 0.7233284 | 0.9984 |
Maximum oxygen consumption
Data manipulation
Inspect file
## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch2
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 8 9 10 12 13 14 15 16 18 19 20 21 22 23
## Minimum and Maximum intervals in uneven Time data:
## [1] 0.94 1.49
## -----------------------------------------
Subset data
cycle1.start <- Cycle_1.mmr[1,1]
cycle1.end <- tail(Cycle_1.mmr, n=1)[1,1]
cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="cycle1_data <- firesting2_mmr |>
subset_data(from = cycle1.start.row,
to = cycle1.end.row,
by = "row") ## subset_data: Multi-column dataset detected in input!
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively.
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.## inspect: Applying column default of 'time = 1'## inspect: Applying column default of 'oxygen = 2'## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch2
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 2 3 4 5 6 8 9 10 11 12 106 107 114 115 116 117 120 121 124
## [20] 125
## Minimum and Maximum intervals in uneven Time data:
## [1] 0.95 1.06
## -----------------------------------------
Calculating MMR
## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
## If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()
##
## # summary.auto_rate # -------------------
##
## === Summary of Results by Highest Rate ===
## rep rank intercept_b0 slope_b1 rsq density row endrow
## <num> <int> <num> <num> <num> <lgcl> <int> <int>
## 1: NA 1 519.9223 -0.06719389 0.9939733 NA 241 301
## 2: NA 2 518.3234 -0.06696975 0.9956304 NA 178 238
## 3: NA 3 517.8726 -0.06689952 0.9955586 NA 179 239
## 4: NA 4 517.9231 -0.06688649 0.9941710 NA 240 300
## 5: NA 5 517.6600 -0.06686161 0.9964188 NA 192 252
## ---
## 237: NA 237 348.5829 -0.04031940 0.9469083 NA 59 119
## 238: NA 238 346.2331 -0.03994745 0.9475506 NA 58 118
## 239: NA 239 343.7430 -0.03955019 0.9453664 NA 55 115
## 240: NA 240 343.6711 -0.03954186 0.9459035 NA 57 117
## 241: NA 241 343.0949 -0.03944957 0.9464607 NA 56 116
## time endtime oxy endoxy rate
## <num> <num> <num> <num> <num>
## 1: 6480.07 6540.07 84.434 80.323 -0.06719389
## 2: 6417.07 6477.07 88.534 84.572 -0.06696975
## 3: 6418.07 6478.07 88.486 84.574 -0.06689952
## 4: 6479.07 6539.07 84.526 80.423 -0.06688649
## 5: 6431.07 6491.07 87.653 83.618 -0.06686161
## ---
## 237: 6298.07 6358.07 94.473 92.006 -0.04031940
## 238: 6297.07 6357.07 94.685 92.045 -0.03994745
## 239: 6294.09 6354.09 95.214 92.207 -0.03955019
## 240: 6296.11 6356.11 94.916 92.088 -0.03954186
## 241: 6295.08 6355.08 95.146 92.073 -0.03944957
##
## Regressions : 241 | Results : 241 | Method : highest | Roll width : 60 | Roll type : time
## -----------------------------------------Adjusting
## adjust_rate: Rate adjustments applied using "mean" method.##
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
##
## Adjustment was applied using the 'mean' method.
##
## Rank 1 of 241 adjusted rate(s):
## Rate : -0.06719389
## Adjustment : -0.00278663
## Adjusted Rate : -0.06440726
##
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------Converting units
mmr_adj2 <- mmr_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253)## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.selecting rates
mmr_final <- mmr_adj2 |>
select_rate(method = "rsq", n=c(0.93,1)) |>
select_rate(method = "highest", n=1) |>
plot(type="full") |>
summary(export=TRUE)## select_rate: Selecting rates with rsq values between 0.93 and 1...## ----- Selection complete. 21 rate(s) removed, 220 rate(s) remaining -----## select_rate: Selecting highest 1 *absolute* rate values...## ----- Selection complete. 219 rate(s) removed, 1 rate(s) remaining -----##
## # plot.convert_rate # -------------------## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: NA 1 519.9223 -0.06719389 0.9939733 NA 241 301 6480.07
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 6540.07 84.434 80.323 -0.06719389 -0.00278663 -0.06440726 -0.06440726
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.04593 0.0009007 NA 35 27 1.013253 -0.6952365
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -771.8847 NA mgO2/hr/kg -771.8847
## -----------------------------------------Results
results <- results |>
mutate(Max_kg = mmr_final$rate.output*-1,
Max = (mmr_final$rate.output*-1)*mass,
rsqmax =mmr_final$rsq,
AAS_kg = Max_kg - Resting_kg,
AAS = Max - Resting,
Notes=Notes,
True_resting="")
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest | Max_kg | Max | rsqmax | AAS_kg | AAS | Notes | True_resting |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 132 | 3 | CVLA468 | CVLA477 | Vlassof reef | 129 | 0.0009007 | ch2 | Dell7440 | 0.04593 | 2023-06-30 | 2024-08-16 | good/good | 35 | 27 | 803.0737 | 0.7233284 | 0.9984 | 771.8847 | 0.6952365 | 0.9939733 | -31.18899 | -0.0280919 |
Exporting data
## Rows: 363 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.4
Enter specimen data
Replicate = 4
mass = 0.0007394
chamber = "ch1"
Swim = "good/good"
chamber_vol = chamber1_dell
system1 = "Dell7440"
Notes=""
##--- time of trail ---##
experiment_mmr_date <- "30 June 2023 12 23PM/Oxygen"
experiment_mmr_date2 <- "30 June 2023 12 23PM/All"
firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell7440/Experiment_",experiment_mmr_date,"data raw/Firesting.txt"),
delim = "\t", escape_double = FALSE,
col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"),
`Time (s)` = col_number(), Ch1...5 = col_number(),
Ch2...6 = col_number(), Ch3...7 = col_number(),
Ch4...8 = col_number()), trim_ws = TRUE,
skip = 19) ## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell7440/Experiment_",experiment_mmr_date2,"slopes/Cycle_1.txt"),
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
`Seconds from start for linreg` = col_number(),
`ch1 po2` = col_number(), `ch2 po2` = col_number(),
`ch3 po2` = col_number(), `ch4 po2` = col_number(),
...8 = col_skip()), trim_ws = TRUE) ## New names:
## • `` -> `...8`Background rates
Pre-experiment
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell7440/Experiment_",preexperiment_date,"slopes"))
pre_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_pre1 <- pre_cycle1 %>% calc_rate.bg()##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------## [1] 8.159452e-05post-experiment
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell7440/Experiment_",postexperiment_date,"slopes"))
post_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_post1 <- post_cycle1 %>% calc_rate.bg() ##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------bg_post <- mean(bg_post1$rate.bg.mean,bg_post3$rate.bg.mean) #bg_post2$rate.bg.mean, # Custom
bg_post ## [1] -0.001419929Resting metabolic rate
Data manipulation
Inspect file
## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch1
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 8 9 10 12 13 14 15 16 18 19 20 21 22 23
## Minimum and Maximum intervals in uneven Time data:
## [1] 0.94 1.49
## -----------------------------------------
#### subset data
Tstart.row=which(firesting2$TIME ==Cycle_1$Time[1], firesting$TIME)
Tstart.dTIME=as.numeric(firesting2[Tstart.row, "dTIME"])
Tend.row=which(firesting2$TIME ==tail(Cycle_last$Time, n=1), firesting$TIME)
Tend.dTIME=as.numeric(firesting2[Tend.row, "dTIME"])
apoly_insp <- firesting2 |>
subset_data(from=Tstart.dTIME,
to=Tend.dTIME,
by="time")
apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)##
## # print.inspect # -----------------------
## dTIME ch1
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 2 3 4 5 6 8 9 10 11 12 106 107 114 115 116 117 120 121 124
## [20] 125
## Minimum and Maximum intervals in uneven Time data:
## [1] 0.95 1.09
## -----------------------------------------
Extract rates
apoly_cr.int <- calc_rate.int(apoly_insp,
starts=(195+45+300),
wait=15,
measure=255,
by="time",
plot=TRUE) ##
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.
## -----------------------------------------##
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from selected replicates...
## To plot others modify 'pos' input.
## -----------------------------------------adjust rates for background
apoly_cr.int_adj <- adjust_rate(apoly_cr.int,
by = bg_pre,
by2 = bg_post,
time_by = Tstart.row,
time_by2 = Tend.row,
method = "linear")## Warning: adjust_rate: background rates in 'by' and 'by2' differ in sign (i.e. one is +ve, one is -ve).
## Ensure this is correct. The 'linear' adjustment has been performed regardless.## adjust_rate: Rate adjustments applied using "linear" method.Converting units
apoly_cr.int_adj2 <- apoly_cr.int_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253) ## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.Plot curve
ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) +
geom_point() +
stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
theme_classic()
Rate filtering
apoly_rmr <- apoly_cr.int_adj2 |>
select_rate(method ="rsq", n=c(0.95,1)) |>
select_rate(method="lowest", n=6) |>
plot(type="full") |>
summary(export = TRUE)## select_rate: Selecting rates with rsq values between 0.95 and 1...## ----- Selection complete. 0 rate(s) removed, 21 rate(s) remaining -----## select_rate: Selecting lowest 6 *absolute* rate values...## ----- Selection complete. 15 rate(s) removed, 6 rate(s) remaining -----##
## # plot.convert_rate # -------------------## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: 7 1 400.1562 -0.03185410 0.997 NA 3256 3511 9495.07
## 2: 8 1 426.1585 -0.03269398 0.997 NA 3796 4051 10035.07
## 3: 9 1 445.4291 -0.03286372 0.994 NA 4336 4591 10575.07
## 4: 17 1 601.1560 -0.03371469 0.960 NA 8656 8911 14895.07
## 5: 19 1 620.4420 -0.03258508 0.998 NA 9736 9991 15975.07
## 6: 21 1 603.2155 -0.02955941 0.974 NA 10816 11071 17055.07
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 9750.07 97.585 89.789 -0.03185410 -0.0003766089 -0.03147749 -0.03147749
## 2: 10290.07 97.973 89.444 -0.03269398 -0.0004496495 -0.03224433 -0.03224433
## 3: 10830.07 98.716 89.483 -0.03286372 -0.0005226900 -0.03234103 -0.03234103
## 4: 15150.07 98.673 89.297 -0.03371469 -0.0011070141 -0.03260767 -0.03260767
## 5: 16230.07 99.686 91.685 -0.03258508 -0.0012530951 -0.03133199 -0.03133199
## 6: 17310.07 99.575 92.544 -0.02955941 -0.0013991762 -0.02816023 -0.02816023
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.0465 0.0007394 NA 35 27 1.013253 -0.3439968
## 2: %Air sec 0.0465 0.0007394 NA 35 27 1.013253 -0.3523770
## 3: %Air sec 0.0465 0.0007394 NA 35 27 1.013253 -0.3534338
## 4: %Air sec 0.0465 0.0007394 NA 35 27 1.013253 -0.3563478
## 5: %Air sec 0.0465 0.0007394 NA 35 27 1.013253 -0.3424066
## 6: %Air sec 0.0465 0.0007394 NA 35 27 1.013253 -0.3077446
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -465.2378 NA mgO2/hr/kg -465.2378
## 2: -476.5716 NA mgO2/hr/kg -476.5716
## 3: -478.0008 NA mgO2/hr/kg -478.0008
## 4: -481.9418 NA mgO2/hr/kg -481.9418
## 5: -463.0872 NA mgO2/hr/kg -463.0872
## 6: -416.2086 NA mgO2/hr/kg -416.2086
## -----------------------------------------Results
results <- data.frame(Clutch = Clutch,
Replicate =Replicate,
Male=Male,
Female=Female,
Population = Population,
Tank = Tank,
Mass = mass,
Chamber = chamber,
System = system1,
Volume = chamber_vol,
Date_tested = Date_tested,
Date_analysed =Date_analysed,
Swim = Swim,
Salinity = salinity,
Temperature = as.numeric(unique(firesting2$temperature)),
Resting_kg = mean(apoly_rmr$rate.output*-1),
Resting = mean(apoly_rmr$rate.output*-1)*mass,
rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 132 | 4 | CVLA468 | CVLA477 | Vlassof reef | 129 | 0.0007394 | ch1 | Dell7440 | 0.0465 | 2023-06-30 | 2024-08-16 | good/good | 35 | 27 | 472.9678 | 0.3497124 | 0.9892 |
Maximum oxygen consumption
Data manipulation
Inspect file
## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch1
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 8 9 10 12 13 14 15 16 18 19 20 21 22 23
## Minimum and Maximum intervals in uneven Time data:
## [1] 0.94 1.49
## -----------------------------------------
Subset data
cycle1.start <- Cycle_1.mmr[1,1]
cycle1.end <- tail(Cycle_1.mmr, n=1)[1,1]
cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="cycle1_data <- firesting2_mmr |>
subset_data(from = cycle1.start.row,
to = cycle1.end.row,
by = "row") ## subset_data: Multi-column dataset detected in input!
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively.
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.## inspect: Applying column default of 'time = 1'## inspect: Applying column default of 'oxygen = 2'## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch1
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 2 3 4 5 6 8 9 10 11 12 106 107 114 115 116 117 120 121 124
## [20] 125
## Minimum and Maximum intervals in uneven Time data:
## [1] 0.95 1.06
## -----------------------------------------
Calculating MMR
## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
## If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()
##
## # summary.auto_rate # -------------------
##
## === Summary of Results by Highest Rate ===
## rep rank intercept_b0 slope_b1 rsq density row endrow
## <num> <int> <num> <num> <num> <lgcl> <int> <int>
## 1: NA 1 469.4146 -0.05943939 0.9888054 NA 9 69
## 2: NA 2 469.3212 -0.05942414 0.9887803 NA 10 70
## 3: NA 3 468.8552 -0.05935100 0.9885545 NA 8 68
## 4: NA 4 468.4228 -0.05928287 0.9883458 NA 7 67
## 5: NA 5 467.8821 -0.05919489 0.9886336 NA 11 71
## ---
## 237: NA 237 301.2047 -0.03296647 0.9547484 NA 170 230
## 238: NA 238 301.0270 -0.03294331 0.9556154 NA 174 234
## 239: NA 239 300.6606 -0.03288567 0.9559458 NA 173 233
## 240: NA 240 300.5061 -0.03285939 0.9561024 NA 171 231
## 241: NA 241 300.1545 -0.03280603 0.9566826 NA 172 232
## time endtime oxy endoxy rate
## <num> <num> <num> <num> <num>
## 1: 6248.07 6308.07 97.965 94.574 -0.05943939
## 2: 6249.07 6309.07 98.029 94.486 -0.05942414
## 3: 6247.07 6307.07 97.926 94.649 -0.05935100
## 4: 6246.07 6306.07 97.973 94.652 -0.05928287
## 5: 6250.07 6310.07 97.925 94.440 -0.05919489
## ---
## 237: 6409.07 6469.07 90.223 87.765 -0.03296647
## 238: 6413.07 6473.07 89.876 87.618 -0.03294331
## 239: 6412.07 6472.07 89.926 87.590 -0.03288567
## 240: 6410.07 6470.07 90.128 87.664 -0.03285939
## 241: 6411.07 6471.07 90.018 87.643 -0.03280603
##
## Regressions : 241 | Results : 241 | Method : highest | Roll width : 60 | Roll type : time
## -----------------------------------------Adjusting
## adjust_rate: Rate adjustments applied using "mean" method.##
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
##
## Adjustment was applied using the 'mean' method.
##
## Rank 1 of 241 adjusted rate(s):
## Rate : -0.05943939
## Adjustment : 8.159452e-05
## Adjusted Rate : -0.05952099
##
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------Converting units
mmr_adj2 <- mmr_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253)## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.selecting rates
mmr_final <- mmr_adj2 |>
select_rate(method = "rsq", n=c(0.93,1)) |>
select_rate(method = "highest", n=1) |>
plot(type="full") |>
summary(export=TRUE)## select_rate: Selecting rates with rsq values between 0.93 and 1...## ----- Selection complete. 0 rate(s) removed, 241 rate(s) remaining -----## select_rate: Selecting highest 1 *absolute* rate values...## ----- Selection complete. 240 rate(s) removed, 1 rate(s) remaining -----##
## # plot.convert_rate # -------------------## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: NA 1 469.4146 -0.05943939 0.9888054 NA 9 69 6248.07
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 6308.07 97.965 94.574 -0.05943939 8.159452e-05 -0.05952099 -0.05952099
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.0465 0.0007394 NA 35 27 1.013253 -0.6504657
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -879.7209 NA mgO2/hr/kg -879.7209
## -----------------------------------------Results
results <- results |>
mutate(Max_kg = mmr_final$rate.output*-1,
Max = (mmr_final$rate.output*-1)*mass,
rsqmax =mmr_final$rsq,
AAS_kg = Max_kg - Resting_kg,
AAS = Max - Resting,
Notes=Notes,
True_resting="")
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest | Max_kg | Max | rsqmax | AAS_kg | AAS | Notes | True_resting |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 132 | 4 | CVLA468 | CVLA477 | Vlassof reef | 129 | 0.0007394 | ch1 | Dell7440 | 0.0465 | 2023-06-30 | 2024-08-16 | good/good | 35 | 27 | 472.9678 | 0.3497124 | 0.9892 | 879.7209 | 0.6504657 | 0.9888054 | 406.7531 | 0.3007532 |
Exporting data
## Rows: 364 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.5
Enter specimen data
Replicate = 5
mass = 0.0008452
chamber = "ch4"
Swim = "good/good"
chamber_vol = chamber4_asus
system1 = "Asus"
Notes=""
##--- time of trail ---##
experiment_mmr_date_asus <- "30 June 2023 12 39PM/Oxygen"
experiment_mmr_date2_asus <- "30 June 2023 12 39PM/All"
firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date_asus,"data raw/Firesting.txt"),
delim = "\t", escape_double = FALSE,
col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"),
`Time (s)` = col_number(), Ch1...5 = col_number(),
Ch2...6 = col_number(), Ch3...7 = col_number(),
Ch4...8 = col_number()), trim_ws = TRUE,
skip = 19) ## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date2_asus,"slopes/Cycle_1.txt"),
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
`Seconds from start for linreg` = col_number(),
`ch1 po2` = col_number(), `ch2 po2` = col_number(),
`ch3 po2` = col_number(), `ch4 po2` = col_number(),
...8 = col_skip()), trim_ws = TRUE) ## New names:
## • `` -> `...8`Background rates
Pre-experiment
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",preexperiment_date_asus,"slopes"))
pre_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_pre1 <- pre_cycle1 %>% calc_rate.bg()##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------## [1] -0.0004462936post-experiment
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",postexperiment_date_asus,"slopes"))
post_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle2 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_post1 <- post_cycle1 %>% calc_rate.bg() ##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------## [1] -0.002127321Resting metabolic rate
Data manipulation
Inspect file
## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch4
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.32 3.81
## -----------------------------------------
#### subset data
Tstart.row=which(firesting2_asus$TIME ==Cycle_1_asus$Time[1], firesting_asus$TIME)
Tstart.dTIME=as.numeric(firesting2_asus[Tstart.row, "dTIME"])
Tend.row=which(firesting2_asus$TIME ==tail(Cycle_last_asus$Time, n=1), firesting_asus$TIME)
Tend.dTIME=as.numeric(firesting2_asus[Tend.row, "dTIME"])
apoly_insp <- firesting2_asus |>
subset_data(from=Tstart.dTIME,
to=Tend.dTIME,
by="time")
apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)##
## # print.inspect # -----------------------
## dTIME ch4
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 10 11 12 13 15 17 19 20 21 22 23 25
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.33 2.34
## -----------------------------------------
Extract rates
apoly_cr.int <- calc_rate.int(apoly_insp,
starts=(195+45+300),
wait=15,
measure=255,
by="time",
plot=TRUE) ##
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.
## -----------------------------------------adjust rates for background
apoly_cr.int_adj <- adjust_rate(apoly_cr.int,
by = bg_pre,
by2 = bg_post,
time_by = Tstart.row,
time_by2 = Tend.row,
method = "linear")## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates.
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.## adjust_rate: Rate adjustments applied using "linear" method.Converting units
apoly_cr.int_adj2 <- apoly_cr.int_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2_asus$temperature)),
P = 1.013253) ## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.Plot curve
ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) +
geom_point() +
stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
theme_classic()
Rate filtering
apoly_rmr <- apoly_cr.int_adj2 |>
select_rate(method ="rsq", n=c(0.95,1)) |>
select_rate(method="lowest", n=6) |>
plot(type="full") |>
summary(export = TRUE)## select_rate: Selecting rates with rsq values between 0.95 and 1...## ----- Selection complete. 0 rate(s) removed, 21 rate(s) remaining -----## select_rate: Selecting lowest 6 *absolute* rate values...## ----- Selection complete. 15 rate(s) removed, 6 rate(s) remaining -----##
## # plot.convert_rate # -------------------## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: 9 1 805.5014 -0.05946600 0.998 NA 3190 3378 11915.55
## 2: 10 1 821.3297 -0.05817231 0.996 NA 3587 3775 12455.11
## 3: 11 1 860.7850 -0.05875232 0.998 NA 3985 4172 12995.42
## 4: 15 1 999.7521 -0.05953733 0.998 NA 5576 5764 15155.41
## 5: 17 1 1056.0264 -0.05904281 0.999 NA 6373 6561 16235.71
## 6: 21 1 1103.3852 -0.05468150 0.999 NA 7960 8148 18395.99
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 12170.65 97.041 81.473 -0.05946600 -0.001773603 -0.05769240 -0.05769240
## 2: 12710.04 97.046 82.430 -0.05817231 -0.001884617 -0.05628769 -0.05628769
## 3: 13249.87 97.087 81.950 -0.05875232 -0.001995754 -0.05675656 -0.05675656
## 4: 15410.13 97.454 82.087 -0.05953733 -0.002440267 -0.05709707 -0.05709707
## 5: 16490.81 97.542 82.338 -0.05904281 -0.002662612 -0.05638020 -0.05638020
## 6: 18651.50 97.725 83.403 -0.05468150 -0.003107199 -0.05157430 -0.05157430
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.04791 0.0008452 NA 35 27 1.013253 -0.6496001
## 2: %Air sec 0.04791 0.0008452 NA 35 27 1.013253 -0.6337835
## 3: %Air sec 0.04791 0.0008452 NA 35 27 1.013253 -0.6390628
## 4: %Air sec 0.04791 0.0008452 NA 35 27 1.013253 -0.6428968
## 5: %Air sec 0.04791 0.0008452 NA 35 27 1.013253 -0.6348251
## 6: %Air sec 0.04791 0.0008452 NA 35 27 1.013253 -0.5807120
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -768.5756 NA mgO2/hr/kg -768.5756
## 2: -749.8621 NA mgO2/hr/kg -749.8621
## 3: -756.1084 NA mgO2/hr/kg -756.1084
## 4: -760.6446 NA mgO2/hr/kg -760.6446
## 5: -751.0945 NA mgO2/hr/kg -751.0945
## 6: -687.0705 NA mgO2/hr/kg -687.0705
## -----------------------------------------Results
results <- data.frame(Clutch = Clutch,
Replicate =Replicate,
Male=Male,
Female=Female,
Population = Population,
Tank = Tank,
Mass = mass,
Chamber = chamber,
System = system1,
Volume = chamber_vol,
Date_tested = Date_tested,
Date_analysed =Date_analysed,
Swim = Swim,
Salinity = salinity,
Temperature = as.numeric(unique(firesting2$temperature)),
Resting_kg = mean(apoly_rmr$rate.output*-1),
Resting = mean(apoly_rmr$rate.output*-1)*mass,
rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 132 | 5 | CVLA468 | CVLA477 | Vlassof reef | 129 | 0.0008452 | ch4 | Asus | 0.04791 | 2023-06-30 | 2024-08-16 | good/good | 35 | 27 | 757.257 | 0.6400336 | 0.9978 |
Maximum oxygen consumption
Data manipulation
Inspect file
## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch4
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.32 3.81
## -----------------------------------------
Subset data
cycle1.start <- Cycle_1.mmr[1,1]
cycle1.end <- tail(Cycle_1.mmr, n=1)[1,1]
cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="cycle1_data <- firesting2_mmr |>
subset_data(from = cycle1.start.row,
to = cycle1.end.row,
by = "row") ## subset_data: Multi-column dataset detected in input!
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively.
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.## inspect: Applying column default of 'time = 1'## inspect: Applying column default of 'oxygen = 2'## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch4
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 5 6 7 8 10 12 13 15 16 17 18 19 20 21 22 23 25
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.32 1.44
## -----------------------------------------
Calculating MMR
## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
## If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()
##
## # summary.auto_rate # -------------------
##
## === Summary of Results by Highest Rate ===
## rep rank intercept_b0 slope_b1 rsq density row endrow
## <num> <int> <num> <num> <num> <lgcl> <int> <int>
## 1: NA 1 984.1874 -0.14782322 0.9946476 NA 164 209
## 2: NA 2 983.5670 -0.14772014 0.9945421 NA 165 210
## 3: NA 3 982.6713 -0.14757948 0.9943805 NA 163 208
## 4: NA 4 981.5143 -0.14738456 0.9941845 NA 166 211
## 5: NA 5 979.5275 -0.14707132 0.9938231 NA 162 207
## ---
## 174: NA 174 470.7572 -0.06346280 0.9759346 NA 76 121
## 175: NA 175 468.6039 -0.06310813 0.9770485 NA 77 122
## 176: NA 176 468.3236 -0.06306854 0.9770397 NA 80 125
## 177: NA 177 467.5655 -0.06293826 0.9776611 NA 78 123
## 178: NA 178 467.2054 -0.06288100 0.9779230 NA 79 124
## time endtime oxy endoxy rate
## <num> <num> <num> <num> <num>
## 1: 6128.64 6188.64 77.954 69.644 -0.14782322
## 2: 6130.00 6190.00 77.864 69.520 -0.14772014
## 3: 6127.27 6187.27 78.069 69.781 -0.14757948
## 4: 6131.34 6191.34 77.721 69.403 -0.14738456
## 5: 6125.94 6185.94 78.162 69.881 -0.14707132
## ---
## 174: 6009.71 6069.71 89.716 85.448 -0.06346280
## 175: 6011.04 6071.04 89.646 85.308 -0.06310813
## 176: 6015.13 6075.13 89.322 84.794 -0.06306854
## 177: 6012.38 6072.38 89.526 85.152 -0.06293826
## 178: 6013.77 6073.77 89.443 84.985 -0.06288100
##
## Regressions : 178 | Results : 178 | Method : highest | Roll width : 60 | Roll type : time
## -----------------------------------------Adjusting
## adjust_rate: Rate adjustments applied using "mean" method.##
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
##
## Adjustment was applied using the 'mean' method.
##
## Rank 1 of 178 adjusted rate(s):
## Rate : -0.1478232
## Adjustment : -0.0004462936
## Adjusted Rate : -0.1473769
##
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------Converting units
mmr_adj2 <- mmr_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253)## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.selecting rates
mmr_final <- mmr_adj2 |>
select_rate(method = "rsq", n=c(0.93,1)) |>
select_rate(method = "highest", n=1) |>
plot(type="full") |>
summary(export=TRUE)## select_rate: Selecting rates with rsq values between 0.93 and 1...## ----- Selection complete. 0 rate(s) removed, 178 rate(s) remaining -----## select_rate: Selecting highest 1 *absolute* rate values...## ----- Selection complete. 177 rate(s) removed, 1 rate(s) remaining -----##
## # plot.convert_rate # -------------------## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: NA 1 984.1874 -0.1478232 0.9946476 NA 164 209 6128.64
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 6188.64 77.954 69.644 -0.1478232 -0.0004462936 -0.1473769 -0.1473769
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.04791 0.0008452 NA 35 27 1.013253 -1.659422
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -1963.349 NA mgO2/hr/kg -1963.349
## -----------------------------------------Results
results <- results |>
mutate(Max_kg = mmr_final$rate.output*-1,
Max = (mmr_final$rate.output*-1)*mass,
rsqmax =mmr_final$rsq,
AAS_kg = Max_kg - Resting_kg,
AAS = Max - Resting,
Notes=Notes,
True_resting="")
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest | Max_kg | Max | rsqmax | AAS_kg | AAS | Notes | True_resting |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 132 | 5 | CVLA468 | CVLA477 | Vlassof reef | 129 | 0.0008452 | ch4 | Asus | 0.04791 | 2023-06-30 | 2024-08-16 | good/good | 35 | 27 | 757.257 | 0.6400336 | 0.9978 | 1963.349 | 1.659422 | 0.9946476 | 1206.092 | 1.019389 | ||
| ### Expor | ting data |
## Rows: 365 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.7
Enter specimen data
Replicate = 7
mass = 0.0006482
chamber = "ch2"
Swim = "good/good"
chamber_vol = chamber2_asus
system1 = "Asus"
Notes=""
##--- time of trail ---##
experiment_mmr_date_asus <- "30 June 2023 12 59PM/Oxygen"
experiment_mmr_date2_asus <- "30 June 2023 12 59PM/All"
firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date_asus,"data raw/Firesting.txt"),
delim = "\t", escape_double = FALSE,
col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"),
`Time (s)` = col_number(), Ch1...5 = col_number(),
Ch2...6 = col_number(), Ch3...7 = col_number(),
Ch4...8 = col_number()), trim_ws = TRUE,
skip = 19) ## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date2_asus,"slopes/Cycle_1.txt"),
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
`Seconds from start for linreg` = col_number(),
`ch1 po2` = col_number(), `ch2 po2` = col_number(),
`ch3 po2` = col_number(), `ch4 po2` = col_number(),
...8 = col_skip()), trim_ws = TRUE) ## New names:
## • `` -> `...8`Background rates
Pre-experiment
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",preexperiment_date_asus,"slopes"))
pre_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_pre1 <- pre_cycle1 %>% calc_rate.bg()##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------## [1] -0.0004651857post-experiment
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",postexperiment_date_asus,"slopes"))
post_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_post1 <- post_cycle1 %>% calc_rate.bg() ##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------## [1] -0.001118812Resting metabolic rate
Data manipulation
Inspect file
## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch2
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.32 3.81
## -----------------------------------------
#### subset data
Tstart.row=which(firesting2_asus$TIME ==Cycle_1_asus$Time[1], firesting_asus$TIME)
Tstart.dTIME=as.numeric(firesting2_asus[Tstart.row, "dTIME"])
Tend.row=which(firesting2_asus$TIME ==tail(Cycle_last_asus$Time, n=1), firesting_asus$TIME)
Tend.dTIME=as.numeric(firesting2_asus[Tend.row, "dTIME"])
apoly_insp <- firesting2_asus |>
subset_data(from=Tstart.dTIME,
to=Tend.dTIME,
by="time")
apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)##
## # print.inspect # -----------------------
## dTIME ch2
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 10 11 12 13 15 17 19 20 21 22 23 25
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.33 2.34
## -----------------------------------------
Extract rates
apoly_cr.int <- calc_rate.int(apoly_insp,
starts=(195+45+300),
wait=15,
measure=245,
by="time",
plot=TRUE) ##
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.
## -----------------------------------------adjust rates for background
apoly_cr.int_adj <- adjust_rate(apoly_cr.int,
by = bg_pre,
by2 = bg_post,
time_by = Tstart.row,
time_by2 = Tend.row,
method = "linear")## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates.
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.## adjust_rate: Rate adjustments applied using "linear" method.Converting units
apoly_cr.int_adj2 <- apoly_cr.int_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253) ## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.Plot curve
ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) +
geom_point() +
stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
theme_classic()
Rate filtering
apoly_rmr <- apoly_cr.int_adj2 |>
select_rate(method ="rsq", n=c(0.95,1)) |>
select_rate(method="lowest", n=6) |>
plot(type="full") |>
summary(export = TRUE)## select_rate: Selecting rates with rsq values between 0.95 and 1...## ----- Selection complete. 10 rate(s) removed, 11 rate(s) remaining -----## select_rate: Selecting lowest 6 *absolute* rate values...## ----- Selection complete. 5 rate(s) removed, 6 rate(s) remaining -----##
## # plot.convert_rate # -------------------## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: 4 1 224.6684 -0.01355650 0.954 NA 1203 1383 9215.99
## 2: 7 1 235.0673 -0.01246661 0.963 NA 2395 2575 10835.35
## 3: 8 1 216.7259 -0.01027239 0.963 NA 2793 2973 11375.89
## 4: 12 1 283.4973 -0.01354605 0.980 NA 4383 4563 13536.08
## 5: 13 1 243.0698 -0.01016629 0.955 NA 4780 4961 14075.19
## 6: 16 1 307.4033 -0.01319405 0.985 NA 5975 6156 15696.00
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 9460.87 99.209 96.015 -0.01355650 -0.0007648689 -0.012791629 -0.012791629
## 2: 11080.07 99.616 96.690 -0.01246661 -0.0008944324 -0.011572179 -0.011572179
## 3: 11620.59 99.510 97.174 -0.01027239 -0.0009376819 -0.009334710 -0.009334710
## 4: 13780.81 99.748 96.577 -0.01354605 -0.0011105264 -0.012435519 -0.012435519
## 5: 14320.64 99.547 97.296 -0.01016629 -0.0011536911 -0.009012595 -0.009012595
## 6: 15941.29 99.910 97.067 -0.01319405 -0.0012833706 -0.011910684 -0.011910684
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.04573 0.0006482 NA 35 27 1.013253 -0.13747646
## 2: %Air sec 0.04573 0.0006482 NA 35 27 1.013253 -0.12437057
## 3: %Air sec 0.04573 0.0006482 NA 35 27 1.013253 -0.10032365
## 4: %Air sec 0.04573 0.0006482 NA 35 27 1.013253 -0.13364921
## 5: %Air sec 0.04573 0.0006482 NA 35 27 1.013253 -0.09686176
## 6: %Air sec 0.04573 0.0006482 NA 35 27 1.013253 -0.12800861
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -212.0896 NA mgO2/hr/kg -212.0896
## 2: -191.8707 NA mgO2/hr/kg -191.8707
## 3: -154.7727 NA mgO2/hr/kg -154.7727
## 4: -206.1851 NA mgO2/hr/kg -206.1851
## 5: -149.4319 NA mgO2/hr/kg -149.4319
## 6: -197.4832 NA mgO2/hr/kg -197.4832
## -----------------------------------------Results
results <- data.frame(Clutch = Clutch,
Replicate =Replicate,
Male=Male,
Female=Female,
Population = Population,
Tank = Tank,
Mass = mass,
Chamber = chamber,
System = system1,
Volume = chamber_vol,
Date_tested = Date_tested,
Date_analysed =Date_analysed,
Swim = Swim,
Salinity = salinity,
Temperature = as.numeric(unique(firesting2$temperature)),
Resting_kg = mean(apoly_rmr$rate.output*-1),
Resting = mean(apoly_rmr$rate.output*-1)*mass,
rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 132 | 7 | CVLA468 | CVLA477 | Vlassof reef | 129 | 0.0006482 | ch2 | Asus | 0.04573 | 2023-06-30 | 2024-08-16 | good/good | 35 | 27 | 192.4803 | 0.1247657 | 0.969 |
Maximum oxygen consumption
Data manipulation
Inspect file
## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch2
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.32 3.81
## -----------------------------------------
Subset data
cycle1.start <- Cycle_1.mmr[1,1]
cycle1.end <- tail(Cycle_1.mmr, n=1)[1,1]
cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="cycle1_data <- firesting2_mmr |>
subset_data(from = cycle1.start.row,
to = cycle1.end.row,
by = "row") ## subset_data: Multi-column dataset detected in input!
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively.
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.## inspect: Applying column default of 'time = 1'## inspect: Applying column default of 'oxygen = 2'## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch2
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.33 1.55
## -----------------------------------------
Calculating MMR
## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
## If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()
##
## # summary.auto_rate # -------------------
##
## === Summary of Results by Highest Rate ===
## rep rank intercept_b0 slope_b1 rsq density row endrow
## <num> <int> <num> <num> <num> <lgcl> <int> <int>
## 1: NA 1 465.9407 -0.05156893 0.9983941 NA 39 84
## 2: NA 2 465.8216 -0.05155194 0.9983806 NA 42 87
## 3: NA 3 465.8051 -0.05154981 0.9983776 NA 40 85
## 4: NA 4 465.7559 -0.05154292 0.9983757 NA 41 86
## 5: NA 5 465.1843 -0.05146349 0.9983344 NA 38 83
## ---
## 173: NA 173 279.7431 -0.02600445 0.9563402 NA 173 218
## 174: NA 174 273.9155 -0.02521204 0.9614417 NA 174 219
## 175: NA 175 268.3858 -0.02446038 0.9678993 NA 175 220
## 176: NA 176 262.7760 -0.02369796 0.9755973 NA 176 221
## 177: NA 177 258.2983 -0.02308952 0.9809714 NA 177 222
## time endtime oxy endoxy rate
## <num> <num> <num> <num> <num>
## 1: 7135.59 7195.59 97.903 94.870 -0.05156893
## 2: 7139.69 7199.69 97.826 94.683 -0.05155194
## 3: 7136.95 7196.95 97.923 94.838 -0.05154981
## 4: 7138.31 7198.31 97.871 94.740 -0.05154292
## 5: 7134.25 7194.25 97.970 94.940 -0.05146349
## ---
## 173: 7317.60 7377.60 89.739 87.979 -0.02600445
## 174: 7319.00 7379.00 89.717 87.944 -0.02521204
## 175: 7320.36 7380.36 89.656 87.908 -0.02446038
## 176: 7321.72 7381.72 89.598 87.894 -0.02369796
## 177: 7323.07 7383.07 89.482 87.866 -0.02308952
##
## Regressions : 177 | Results : 177 | Method : highest | Roll width : 60 | Roll type : time
## -----------------------------------------Adjusting
## adjust_rate: Rate adjustments applied using "mean" method.##
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
##
## Adjustment was applied using the 'mean' method.
##
## Rank 1 of 177 adjusted rate(s):
## Rate : -0.05156893
## Adjustment : -0.0004651857
## Adjusted Rate : -0.05110374
##
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------Converting units
mmr_adj2 <- mmr_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253)## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.selecting rates
mmr_final <- mmr_adj2 |>
select_rate(method = "rsq", n=c(0.93,1)) |>
select_rate(method = "highest", n=1) |>
plot(type="full") |>
summary(export=TRUE)## select_rate: Selecting rates with rsq values between 0.93 and 1...## ----- Selection complete. 0 rate(s) removed, 177 rate(s) remaining -----## select_rate: Selecting highest 1 *absolute* rate values...## ----- Selection complete. 176 rate(s) removed, 1 rate(s) remaining -----##
## # plot.convert_rate # -------------------## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: NA 1 465.9407 -0.05156893 0.9983941 NA 39 84 7135.59
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 7195.59 97.903 94.87 -0.05156893 -0.0004651857 -0.05110374 -0.05110374
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.04573 0.0006482 NA 35 27 1.013253 -0.5492312
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -847.3175 NA mgO2/hr/kg -847.3175
## -----------------------------------------Results
results <- results |>
mutate(Max_kg = mmr_final$rate.output*-1,
Max = (mmr_final$rate.output*-1)*mass,
rsqmax =mmr_final$rsq,
AAS_kg = Max_kg - Resting_kg,
AAS = Max - Resting,
Notes=Notes,
True_resting="")
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest | Max_kg | Max | rsqmax | AAS_kg | AAS | Notes | True_resting |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 132 | 7 | CVLA468 | CVLA477 | Vlassof reef | 129 | 0.0006482 | ch2 | Asus | 0.04573 | 2023-06-30 | 2024-08-16 | good/good | 35 | 27 | 192.4803 | 0.1247657 | 0.969 | 847.3175 | 0.5492312 | 0.9983941 | 654.8372 | 0.4244655 | ||
| ### Expor | ting data |
## Rows: 366 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.8
Enter specimen data
Replicate = 8
mass = 0.0006201
chamber = "ch1"
Swim = "good/good"
chamber_vol = chamber1_asus
system1 = "Asus"
Notes=""
##--- time of trail ---##
experiment_mmr_date_asus <- "30 June 2023 01 07PM/Oxygen"
experiment_mmr_date2_asus <- "30 June 2023 01 07PM/All"
firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date_asus,"data raw/Firesting.txt"),
delim = "\t", escape_double = FALSE,
col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"),
`Time (s)` = col_number(), Ch1...5 = col_number(),
Ch2...6 = col_number(), Ch3...7 = col_number(),
Ch4...8 = col_number()), trim_ws = TRUE,
skip = 19) ## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date2_asus,"slopes/Cycle_1.txt"),
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
`Seconds from start for linreg` = col_number(),
`ch1 po2` = col_number(), `ch2 po2` = col_number(),
`ch3 po2` = col_number(), `ch4 po2` = col_number(),
...8 = col_skip()), trim_ws = TRUE) ## New names:
## • `` -> `...8`Background rates
Pre-experiment
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",preexperiment_date_asus,"slopes"))
pre_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_pre1 <- pre_cycle1 %>% calc_rate.bg()##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------## [1] -0.001027631post-experiment
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",postexperiment_date_asus,"slopes"))
post_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_post1 <- post_cycle1 %>% calc_rate.bg() ##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------## [1] -0.003359184Resting metabolic rate
Data manipulation
Inspect file
## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch1
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.32 3.81
## -----------------------------------------
#### subset data
Tstart.row=which(firesting2_asus$TIME ==Cycle_1_asus$Time[1], firesting_asus$TIME)
Tstart.dTIME=as.numeric(firesting2_asus[Tstart.row, "dTIME"])
Tend.row=which(firesting2_asus$TIME ==tail(Cycle_last_asus$Time, n=1), firesting_asus$TIME)
Tend.dTIME=as.numeric(firesting2_asus[Tend.row, "dTIME"])
apoly_insp <- firesting2_asus |>
subset_data(from=Tstart.dTIME,
to=Tend.dTIME,
by="time")
apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)##
## # print.inspect # -----------------------
## dTIME ch1
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 10 11 12 13 15 17 19 20 21 22 23 25
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.33 2.34
## -----------------------------------------
Extract rates
apoly_cr.int <- calc_rate.int(apoly_insp,
starts=(195+45+300),
wait=15,
measure=245,
by="time",
plot=TRUE) ##
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.
## -----------------------------------------adjust rates for background
apoly_cr.int_adj <- adjust_rate(apoly_cr.int,
by = bg_pre,
by2 = bg_post,
time_by = Tstart.row,
time_by2 = Tend.row,
method = "linear")## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates.
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.## adjust_rate: Rate adjustments applied using "linear" method.Converting units
apoly_cr.int_adj2 <- apoly_cr.int_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253) ## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.Plot curve
ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) +
geom_point() +
stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
theme_classic()
Rate filtering
apoly_rmr <- apoly_cr.int_adj2 |>
select_rate(method ="rsq", n=c(0.95,1)) |>
select_rate(method="lowest", n=6) |>
plot(type="full") |>
summary(export = TRUE)## select_rate: Selecting rates with rsq values between 0.95 and 1...## ----- Selection complete. 1 rate(s) removed, 20 rate(s) remaining -----## select_rate: Selecting lowest 6 *absolute* rate values...## ----- Selection complete. 14 rate(s) removed, 6 rate(s) remaining -----##
## # plot.convert_rate # -------------------## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: 11 1 411.8558 -0.02414850 0.980 NA 3985 4165 12995.42
## 2: 12 1 392.5206 -0.02176415 0.974 NA 4383 4563 13536.08
## 3: 18 1 483.0453 -0.02291465 0.994 NA 6771 6948 16776.08
## 4: 19 1 483.2968 -0.02220922 0.970 NA 7164 7344 17314.92
## 5: 20 1 456.4901 -0.02008391 0.989 NA 7562 7742 17855.20
## 6: 21 1 569.0287 -0.02558204 0.967 NA 7960 8140 18395.99
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 13240.33 97.906 92.137 -0.02414850 -0.003175341 -0.02097316 -0.02097316
## 2: 13780.81 97.512 92.104 -0.02176415 -0.003329627 -0.01843452 -0.01843452
## 3: 17021.04 98.245 93.169 -0.02291465 -0.004254404 -0.01866025 -0.01866025
## 4: 17559.55 98.372 92.705 -0.02220922 -0.004408150 -0.01780107 -0.01780107
## 5: 18099.81 98.060 92.758 -0.02008391 -0.004562351 -0.01552156 -0.01552156
## 6: 18640.70 97.999 91.499 -0.02558204 -0.004716714 -0.02086533 -0.02086533
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.04565 0.0006201 NA 35 27 1.013253 -0.2250121
## 2: %Air sec 0.04565 0.0006201 NA 35 27 1.013253 -0.1977762
## 3: %Air sec 0.04565 0.0006201 NA 35 27 1.013253 -0.2001979
## 4: %Air sec 0.04565 0.0006201 NA 35 27 1.013253 -0.1909801
## 5: %Air sec 0.04565 0.0006201 NA 35 27 1.013253 -0.1665242
## 6: %Air sec 0.04565 0.0006201 NA 35 27 1.013253 -0.2238553
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -362.8643 NA mgO2/hr/kg -362.8643
## 2: -318.9424 NA mgO2/hr/kg -318.9424
## 3: -322.8477 NA mgO2/hr/kg -322.8477
## 4: -307.9828 NA mgO2/hr/kg -307.9828
## 5: -268.5442 NA mgO2/hr/kg -268.5442
## 6: -360.9987 NA mgO2/hr/kg -360.9987
## -----------------------------------------Results
results <- data.frame(Clutch = Clutch,
Replicate =Replicate,
Male=Male,
Female=Female,
Population = Population,
Tank = Tank,
Mass = mass,
Chamber = chamber,
System = system1,
Volume = chamber_vol,
Date_tested = Date_tested,
Date_analysed =Date_analysed,
Swim = Swim,
Salinity = salinity,
Temperature = as.numeric(unique(firesting2$temperature)),
Resting_kg = mean(apoly_rmr$rate.output*-1),
Resting = mean(apoly_rmr$rate.output*-1)*mass,
rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 132 | 8 | CVLA468 | CVLA477 | Vlassof reef | 129 | 0.0006201 | ch1 | Asus | 0.04565 | 2023-06-30 | 2024-08-16 | good/good | 35 | 27 | 334.7272 | 0.2075643 | 0.977 |
Maximum oxygen consumption
Data manipulation
Inspect file
## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch1
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.32 3.81
## -----------------------------------------
Subset data
cycle1.start <- Cycle_1.mmr[1,1]
cycle1.end <- tail(Cycle_1.mmr, n=1)[1,1]
cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="cycle1_data <- firesting2_mmr |>
subset_data(from = cycle1.start.row,
to = cycle1.end.row,
by = "row") ## subset_data: Multi-column dataset detected in input!
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively.
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.## inspect: Applying column default of 'time = 1'## inspect: Applying column default of 'oxygen = 2'## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch1
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 5 6 7 8 9 10 11 13 14 15 16 17 18 20 21 22 23
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.33 1.78
## -----------------------------------------
Calculating MMR
## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
## If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()
##
## # summary.auto_rate # -------------------
##
## === Summary of Results by Highest Rate ===
## rep rank intercept_b0 slope_b1 rsq density row endrow
## <num> <int> <num> <num> <num> <lgcl> <int> <int>
## 1: NA 1 453.8062 -0.04661341 0.9942947 NA 137 182
## 2: NA 2 453.5200 -0.04657740 0.9941775 NA 136 181
## 3: NA 3 452.7614 -0.04656481 0.9600424 NA 83 128
## 4: NA 4 452.6005 -0.04654266 0.9598490 NA 84 129
## 5: NA 5 451.6137 -0.04641768 0.9587519 NA 82 127
## ---
## 173: NA 173 316.9513 -0.02892906 0.9408273 NA 54 99
## 174: NA 174 315.7212 -0.02885545 0.9679076 NA 103 148
## 175: NA 175 315.3978 -0.02881661 0.9680774 NA 106 151
## 176: NA 176 314.6106 -0.02871296 0.9690958 NA 104 149
## 177: NA 177 313.6205 -0.02858634 0.9707145 NA 105 150
## time endtime oxy endoxy rate
## <num> <num> <num> <num> <num>
## 1: 7766.88 7826.88 91.615 89.080 -0.04661341
## 2: 7765.52 7825.52 91.634 89.088 -0.04657740
## 3: 7693.03 7753.03 94.284 91.966 -0.04656481
## 4: 7694.40 7754.40 94.304 91.918 -0.04654266
## 5: 7691.70 7751.70 94.323 92.044 -0.04641768
## ---
## 173: 7653.72 7713.72 95.899 93.549 -0.02892906
## 174: 7720.61 7780.61 93.168 91.121 -0.02885545
## 175: 7724.70 7784.70 93.061 90.844 -0.02881661
## 176: 7721.99 7781.99 93.112 91.067 -0.02871296
## 177: 7723.35 7783.35 93.062 90.969 -0.02858634
##
## Regressions : 177 | Results : 177 | Method : highest | Roll width : 60 | Roll type : time
## -----------------------------------------Adjusting
## adjust_rate: Rate adjustments applied using "mean" method.##
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
##
## Adjustment was applied using the 'mean' method.
##
## Rank 1 of 177 adjusted rate(s):
## Rate : -0.04661341
## Adjustment : -0.001027631
## Adjusted Rate : -0.04558578
##
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------Converting units
mmr_adj2 <- mmr_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253)## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.selecting rates
mmr_final <- mmr_adj2 |>
select_rate(method = "rsq", n=c(0.93,1)) |>
select_rate(method = "highest", n=1) |>
plot(type="full") |>
summary(export=TRUE)## select_rate: Selecting rates with rsq values between 0.93 and 1...## ----- Selection complete. 17 rate(s) removed, 160 rate(s) remaining -----## select_rate: Selecting highest 1 *absolute* rate values...## ----- Selection complete. 159 rate(s) removed, 1 rate(s) remaining -----##
## # plot.convert_rate # -------------------## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: NA 1 453.8062 -0.04661341 0.9942947 NA 137 182 7766.88
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 7826.88 91.615 89.08 -0.04661341 -0.001027631 -0.04558578 -0.04558578
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.04565 0.0006201 NA 35 27 1.013253 -0.4890705
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -788.6962 NA mgO2/hr/kg -788.6962
## -----------------------------------------Results
results <- results |>
mutate(Max_kg = mmr_final$rate.output*-1,
Max = (mmr_final$rate.output*-1)*mass,
rsqmax =mmr_final$rsq,
AAS_kg = Max_kg - Resting_kg,
AAS = Max - Resting,
Notes=Notes,
True_resting="")
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest | Max_kg | Max | rsqmax | AAS_kg | AAS | Notes | True_resting |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 132 | 8 | CVLA468 | CVLA477 | Vlassof reef | 129 | 0.0006201 | ch1 | Asus | 0.04565 | 2023-06-30 | 2024-08-16 | good/good | 35 | 27 | 334.7272 | 0.2075643 | 0.977 | 788.6962 | 0.4890705 | 0.9942947 | 453.969 | 0.2815062 | ||
| ### Expor | ting data |
## Rows: 367 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.